地球信息科学学报 ›› 2013, Vol. 15 ›› Issue (3): 431-439.doi: 10.3724/SP.J.1047.2013.00431

• 遥感科学与应用技术 • 上一篇    下一篇

通用劈窗算法的NOAA-18(N)AVHRR/3数据地表温度遥感反演与验证

孙志伟1,2, 唐伯惠2, 吴骅2, 程耀东1   

  1. 1. 兰州交通大学测绘与地理信息学院,兰州730070;
    2. 中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京100101
  • 收稿日期:2012-12-04 修回日期:2013-03-11 出版日期:2013-06-25 发布日期:2013-06-17
  • 通讯作者: 唐伯惠(1973-),男,湖南永州人,博士,副研究员,研究方向为地表参数的遥感定量反演,以及地表净辐射、蒸散发和土壤水分的遥感估算方法研究。E-mail:tangbh@igsnrr.ac.cn E-mail:tangbh@igsnrr.ac.cn
  • 作者简介:孙志伟(1988-),男,山东临沂人,硕士生,研究方向为地表参数的遥感定量反演、GIS应用及开发。E-mail:sunzw@lreis.ac.cn
  • 基金资助:

    国家自然科学基金面上项目(41171287);中国科学院战略性先导科技专项(XDA05050109)。

Retrieval and Validation of Land Surface Temperature with General Split-window Algorithm from NOAA-18(N) AVHRR/3 Data

SUN Zhiwei1,2, TANG Bohui2, WU Hua2, CHENG Yaodong1   

  1. 1. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China;
    2. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • Received:2012-12-04 Revised:2013-03-11 Online:2013-06-25 Published:2013-06-17

摘要:

本文以NOAA-18(N)AVHRR/3 数据,运用通用劈窗技术获得地表温度。首先,利用MODTRAN 4 模拟不同地表和大气状况下热红外通道(Ch4,10.3~11.3μm和Ch5,11.5~12.5μm)的星上亮温,并建立模拟数据库。其次,按照地表温度、大气可降水汽含量、地表比辐射率和观测天顶角,对模拟数据库分组,确定出各分组的通用劈窗算法系数。然后,将构建的地表温度反演模型应用到NOAA-18(N)AVHRR/3 数据,模型所需的地表比辐射率由NDVI阈值法确定,大气可降水汽含量是利用Li 等(2003)提出的一种劈窗的协方差与方差比的方法来估算。反演结果表明:在观测天顶角小于30°或者大气可降水汽含量小于3.5 g/cm2时,地表温度反演的均方根误差小于1.0K;在观测天顶角小于45°并且大气可降水汽含量小于5.5g/cm2情况下,均方根误差小于1.5K。最后,利用美国通量站的实测数据对地表温度反演结果进行了验证,结果表明均方根误差小于1.8K。

关键词: AVHRR/3, NOAA-18(N), 通用劈窗算法, 地表温度

Abstract:

On the basis of the atmospheric radiative transfer theory, land surface temperature (LST) was retrieved from NOAA-18 (N) AVHRR/3 cloud-free data using a general split-window (GSW) algorithm. A simulated database including the thermal infra-red channels (Channel 4 with spectral range 10.3-11.3μm and Channel 5with spectral range11.5-12.5μm) radiance observed at the satellite level was established with the atmospheric radiative transfer model MODTRAN 4. It covers various land surface types and atmospheric conditions. Then the coefficients of the GSW were determined by grouping the LST, the atmospheric water vapor content (WVC), the land surface emissivity (LSE), and the viewing zenith angle (VZA) by several sub-ranges. Results showed that the root mean square error (RMSE) between the estimated and actual LST is less than 1.0K when the VZA is less than 30° or the WVC is less than 3.5 g/cm2. The RMSE is less than 1.5K when the VZA is less than 45° and the WVC is less than 5.5 g/cm2. In addition, the proposed algorithm was applied to the NOAA-18 (N) AVHRR/3 data to retrieve LSTs. The LSE was determined by using the NDVI thresholds method, and the WVC was estimated with the transmittance ratio method proposed by Li et al. (2003). Finally, the field measured data of the US-Flux stations were used to validate the retrieved LST. The results showed that the RMSE is less than 1.8K.

Key words: land surface temperature, general split-window algorithm, AVHRR/3, NOAA-18(N)